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# multiAgents.py
# --------------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
from util import manhattanDistance
from game import Directions
import random, util
from game import Agent
class ReflexAgent(Agent):
"""
A reflex agent chooses an action at each choice point by examining
its alternatives via a state evaluation function.
The code below is provided as a guide. You are welcome to change
it in any way you see fit, so long as you don't touch our method
headers.
"""
def getAction(self, gameState):
"""
You do not need to change this method, but you're welcome to.
getAction chooses among the best options according to the evaluation function.
Just like in the previous project, getAction takes a GameState and returns
some Directions.X for some X in the set {NORTH, SOUTH, WEST, EAST, STOP}
"""
# Collect legal moves and successor states
legalMoves = gameState.getLegalActions()
# Choose one of the best actions
scores = [self.evaluationFunction(gameState, action) for action in legalMoves]
bestScore = max(scores)
bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]
chosenIndex = random.choice(bestIndices) # Pick randomly among the best
"Add more of your code here if you want to"
return legalMoves[chosenIndex]
def evaluationFunction(self, currentGameState, action):
"""
Design a better evaluation function here.
The evaluation function takes in the current and proposed successor
GameStates (pacman.py) and returns a number, where higher numbers are better.
The code below extracts some useful information from the state, like the
remaining food (newFood) and Pacman position after moving (newPos).
newScaredTimes holds the number of moves that each ghost will remain
scared because of Pacman having eaten a power pellet.
Print out these variables to see what you're getting, then combine them
to create a masterful evaluation function.
"""
# Useful information you can extract from a GameState (pacman.py)
successorGameState = currentGameState.generatePacmanSuccessor(action)
newPos = successorGameState.getPacmanPosition()
newFood = successorGameState.getFood()
newGhostStates = successorGameState.getGhostStates()
newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]
"*** YOUR CODE HERE ***"
closestFoodDistance, closestGhostDistance = 1e9, 1e9
foodDistances, ghostDistances = [closestFoodDistance], [closestGhostDistance]
foodList = newFood.asList()
currentFoodNumber = len(currentGameState.getFood().asList())
for food in foodList:
foodDistances.append(util.manhattanDistance(newPos, food))
closestFoodDistance = min(foodDistances)
for ghost in newGhostStates:
ghostDistances.append(util.manhattanDistance(newPos, ghost.getPosition()))
closestGhostDistance = min(ghostDistances)
foodDiff = currentFoodNumber - len(foodList)
evalScore = 1/(closestFoodDistance + 1)
if closestGhostDistance <= 1:
return -1e9
elif foodDiff > 0:
return 1e9
else:
return evalScore
def scoreEvaluationFunction(currentGameState):
"""
This default evaluation function just returns the score of the state.
The score is the same one displayed in the Pacman GUI.
This evaluation function is meant for use with adversarial search agents
(not reflex agents).
"""
return currentGameState.getScore()
class MultiAgentSearchAgent(Agent):
"""
This class provides some common elements to all of your
multi-agent searchers. Any methods defined here will be available
to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.
You *do not* need to make any changes here, but you can if you want to
add functionality to all your adversarial search agents. Please do not
remove anything, however.
Note: this is an abstract class: one that should not be instantiated. It's
only partially specified, and designed to be extended. Agent (game.py)
is another abstract class.
"""
def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):
self.index = 0 # Pacman is always agent index 0
self.evaluationFunction = util.lookup(evalFn, globals())
self.depth = int(depth)
class MinimaxAgent(MultiAgentSearchAgent):
"""
Your minimax agent (question 2)
"""
def getAction(self, gameState):
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction.
Here are some method calls that might be useful when implementing minimax.
gameState.getLegalActions(agentIndex):
Returns a list of legal actions for an agent
agentIndex=0 means Pacman, ghosts are >= 1
gameState.generateSuccessor(agentIndex, action):
Returns the successor game state after an agent takes an action
gameState.getNumAgents():
Returns the total number of agents in the game
gameState.isWin():
Returns whether or not the game state is a winning state
gameState.isLose():
Returns whether or not the game state is a losing state
"""
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
return self.value(gameState, 0, self.depth)[1]
def value(self, gameState, agentIndex, depth):
if gameState.isWin() or gameState.isLose() or depth == 0:
return self.evaluationFunction(gameState), Directions.STOP
elif agentIndex == 0:
return self.maxValue(gameState, agentIndex, depth)
else:
return self.minValue(gameState, agentIndex, depth)
def minValue(self, gameState, agentIndex, depth):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
selectedScore, selectedAction = 1e9, Directions.STOP
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth)[0]
if successorScore < selectedScore:
selectedScore = successorScore
selectedAction = action
return selectedScore, selectedAction
def maxValue(self, gameState, agentIndex, depth):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
selectedScore, selectedAction = -1e9, Directions.STOP
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth)[0]
if successorScore > selectedScore:
selectedScore = successorScore
selectedAction = action
return selectedScore, selectedAction
class AlphaBetaAgent(MultiAgentSearchAgent):
"""
Your minimax agent with alpha-beta pruning (question 3)
"""
def getAction(self, gameState):
"""
Returns the minimax action using self.depth and self.evaluationFunction
"""
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
return self.value(gameState, 0, self.depth, -1e9, 1e9)[1]
def value(self, gameState, agentIndex, depth, alpha, beta):
if gameState.isWin() or gameState.isLose() or depth == 0:
return self.evaluationFunction(gameState), Directions.STOP
elif agentIndex == 0:
return self.maxValue(gameState, agentIndex, depth, alpha, beta)
else:
return self.minValue(gameState, agentIndex, depth, alpha, beta)
def minValue(self, gameState, agentIndex, depth, alpha, beta):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
selectedScore, selectedAction = 1e9, Directions.STOP
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth, alpha, beta)[0]
if successorScore < selectedScore:
selectedScore = successorScore
selectedAction = action
if selectedScore < alpha:
return selectedScore, selectedAction
beta = min(beta, selectedScore)
return selectedScore, selectedAction
def maxValue(self, gameState, agentIndex, depth, alpha, beta):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
selectedScore, selectedAction = -1e9, Directions.STOP
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth, alpha, beta)[0]
if successorScore > selectedScore:
selectedScore = successorScore
selectedAction = action
if selectedScore > beta:
return selectedScore, selectedAction
alpha = max(alpha, selectedScore)
return selectedScore, selectedAction
class ExpectimaxAgent(MultiAgentSearchAgent):
"""
Your expectimax agent (question 4)
"""
def getAction(self, gameState):
"""
Returns the expectimax action using self.depth and self.evaluationFunction
All ghosts should be modeled as choosing uniformly at random from their
legal moves.
"""
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
return self.value(gameState, 0, self.depth)[1]
def value(self, gameState, agentIndex, depth):
if gameState.isWin() or gameState.isLose() or depth == 0:
return self.evaluationFunction(gameState), Directions.STOP
elif agentIndex == 0:
return self.maxValue(gameState, agentIndex, depth)
else:
return self.expValue(gameState, agentIndex, depth)
def expValue(self, gameState, agentIndex, depth):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
# selectedScore, selectedAction = 1e9, Directions.STOP
expectedValue, prob = 0, 0
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth)[0]
prob = 1/len(actionList)
expectedValue = expectedValue + prob * successorScore # formula of expected value
return expectedValue, random.choice(actionList)
def maxValue(self, gameState, agentIndex, depth):
nextAgent = (agentIndex + 1) % gameState.getNumAgents()
if nextAgent == 0:
nextDepth = depth - 1
else:
nextDepth = depth
actionList = gameState.getLegalActions(agentIndex)
selectedScore, selectedAction = -1e9, Directions.STOP
for action in actionList:
successorGameState = gameState.generateSuccessor(agentIndex, action)
successorScore = self.value(successorGameState, nextAgent, nextDepth)[0]
if successorScore > selectedScore:
selectedScore = successorScore
selectedAction = action
return selectedScore, selectedAction
def betterEvaluationFunction(currentGameState):
"""
Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable
evaluation function (question 5).
DESCRIPTION: <write something here so we know what you did>
"""
"*** YOUR CODE HERE ***"
# util.raiseNotDefined()
if currentGameState.isWin():
return 1e9
elif currentGameState.isLose():
return -1e9
currPos = currentGameState.getPacmanPosition()
currFood = currentGameState.getFood()
currGhostStates = currentGameState.getGhostStates()
currScore = currentGameState.getScore()
currCapsList = currentGameState.getCapsules()
currFoodList = currFood.asList()
closestFoodDistance, closestGhostDistance, closestCapsuleDistance = 1e9, 1e9, 1e9
foodDistances, ghostDistances, capsuleDistances = [closestFoodDistance], [closestGhostDistance], [closestCapsuleDistance]
foodCoeff = 1
capsuleCoeff = 1
scoreCoeff = 1
ghostDistCoeff = 2
scaredGhostTimer = 4
# closest food
for food in currFoodList:
foodDistances.append(util.manhattanDistance(currPos, food))
closestFoodDistance = min(foodDistances)
# closest capsule
for capsule in currCapsList:
capsuleDistances.append(util.manhattanDistance(currPos, capsule))
closestCapsuleDistance = min(capsuleDistances)
# dealing with ghost
for ghost in currGhostStates:
ghostDistance = util.manhattanDistance(currPos, ghost.getPosition())
if ghostDistance <= ghostDistCoeff and ghost.scaredTimer > scaredGhostTimer:
return 1e9
elif ghostDistance <= ghostDistCoeff and ghost.scaredTimer <= scaredGhostTimer:
return -1e9
evalScore = foodCoeff * (1/(closestFoodDistance + 1)) + capsuleCoeff * 1/(closestCapsuleDistance + 1) + scoreCoeff * currScore
return evalScore
# Abbreviation
better = betterEvaluationFunctionEditor is loading...